Energy efficiency and robustness of locomotion to different terrain conditions are important problems for humanoid robots deployed in the real world. In this paper, we propose a footstep-planning algorithm for humanoids that is applicable to flat, slanted, and slippery terrain, which uses simple principles and representations gathered from human gait literature. The planner optimizes a center-of-mass (COM) mechanical work model subject to motion feasibility and ground friction constraints using a hybrid A* search and optimization approach. Footstep placements and orientations are discrete states searched with an A* algorithm, while other relevant parameters are computed through continuous optimization on state transitions. These parameters are also inspired by human gait literature and include footstep timing (double-support and swing time) and parameterized COM motion using knee flexion angle keypoints. The planner relies on work, the required coefficient of friction (RCOF), and feasibility models that we estimate in a physics simulation. We show through simulation experiments that the proposed planner leads to both low electrical energy consumption and human-like motion on a variety of scenarios. Using the planner, the robot automatically opts between avoiding or (slowly) traversing slippery patches depending on their size and friction, and it chooses energy-optimal stairs and climbing angles in slopes. The obtained motion is also consistent with observations found in human gait literature, such as human-like changes in RCOF, step length and double-support time on slippery terrain, and human-like curved walking on steep slopes. Finally, we compare COM work minimization with other choices of the objective function.

Human–Robot Interaction (HRI) using hand gesture recognition offers an effective and non-contact approach to enhancing operational intuitiveness and user convenience. However, most existing studies primarily focus on either static sign language recognition or the tracking of hand position and orientation in space. These approaches often prove inadequate for controlling complex robotic systems. This paper proposes an advanced HRI system leveraging dynamic hand gestures for controlling quadruped robots equipped with a robotic arm. The proposed system integrates both semantic and pose information from dynamic gestures to enable comprehensive control over the robot’s diverse functionalities. First, a Depth–MediaPipe framework is introduced to facilitate the precise three-dimensional (3D) coordinate extraction of 21 hand bone keypoints. Subsequently, a Semantic-Pose to Motion (SPM) model is developed to analyze and interpret both the pose and semantic aspects of hand gestures. This model translates the extracted 3D coordinate data into corresponding mechanical actions in real-time, encompassing quadruped robot locomotion, robotic arm end-effector tracking, and semantic-based command switching. Extensive real-world experiments demonstrate the proposed system’s effectiveness in achieving real-time interaction and precise control, underscoring its potential for enhancing the usability of complex robotic platforms.

The effects of mechanical system dynamics are often disregarded in the design process of humanoid robots. Sophisticated control methods may compensate for some limitations of the mechanical structure, however, principal limitations of the system performance can arise from poor mechanical architecture. Therefore, it is important to develop robot hardware that behaves close to an ideal model and that is easy to be modeled from the viewpoint of mechanics. This paper surveys the mechanics of humanoid robots from the viewpoint of joint mechanism, kinematic structure of the leg joints, and foot mechanisms. Firstly, the actuators and power transmission mechanisms for driving the joints are summarized. For the kinematic structure of leg joints, the characteristics of serial and parallel mechanisms are explained, and the specific configuration of the hip, knee and ankle joints are introduced. In order to make a robot move as close to the ideal movement as possible, we need to design the robot to reduce backlash at each joint. Various foot mechanisms are also introduced in the paper.

Many extant studies proposed various stabilizing control methods for humanoids during the stance phase while hopping and running. Although these methods contribute to stability during hopping and running, humanoid robots do not swing their legs rapidly during the flight phase to prevent rotation in the yaw direction. Humans utilize their torsos and arms when running to compensate for the angular momentum in the yaw direction generated by leg movement during the flight phase. In this study, we developed an angular momentum control method based on human motion for a humanoid upper body. The method involves calculation of the angular momentum generated by the movement of the humanoid legs and calculation of the torso and arm motions required to compensate for the angular momentum of the legs in the yaw direction. We also developed a humanoid upper-body mechanism having human link length and mass properties, using carbon-fiber-reinforced plastic and a symmetric structure for generating large angular momentum. The humanoid robot developed in this study could generate almost the same angular momentum as that of a human. Furthermore, when suspended in midair, the humanoid robot achieved angular momentum compensation in the yaw direction.

Walking robots have been widely applied in complex terrains due to their good terrain adaptability and trafficability. However, in some environments (such as disaster relief, field navigation, etc.), although a single strategy can adapt to various environments, it is unable to strike a balance between speed and stability. Existing control schemes like model predictive control (MPC) and traditional incremental control can manage certain environments. However, they often cannot balance speed and stability well. These methods usually rely on a single strategy and lack adaptability for dynamic adjustment to different terrains. To address this limitation, this paper proposes an innovative double-layer reinforcement learning algorithm. This algorithm combines Deep Double Q-Network (DDQN) and Proximal Policy Optimization (PPO), leveraging their complementary strengths to achieve both fast adaptation and high stability in complex terrains. This algorithm utilizes terrain information and the robot’s state as observations, determines the walking speed command of the quadruped robot Unitree Go1 through DDQN, and dynamically adjusts the current walking speed in complex terrains based on the robot action control system of PPO. The speed command serves as a crucial link between the robot’s perception and movement, guiding how fast the robot should walk depending on the environment and its internal state. By using DDQN, the algorithm ensures that the robot can set an appropriate speed based on what it observes, such as changes in terrain or obstacles. PPO then executes this speed, allowing the robot to navigate in real time over difficult or uneven surfaces, ensuring smooth and stable movement. Then, the proposed model is verified in detail in Isaac Gym. Wecompare the distances walked by the robot using six different control methods within 10 s. The experimental results indicate that the method proposed in this paper demonstrates excellent speed adjustment ability in complex terrains. On the designed test route, the quadruped robot Unitree Go1 can not only maintain a high walking speed but also maintain a high degree of stability when switching between different terrains. Ouralgorithm helps the robot walk 25.5 m in 10 s, outperforming other methods.

This study describes a biped walking stabilization based on gait analysis for a humanoid robot. So far, we have developed a humanoid robot as a human motion simulator which can quantitatively evaluate welfare and rehabilitation instruments instead of human subjects. However, the walking motion of the robot looked like human’s in our past researches, but a walking stabilization control was not based on gait analysis. To use a humanoid robot as a human motion simulator, not only mechanisms but also a stabilizer should be designed based on human beings. Of course, there are many studies on gait analysis in the field of neuroscience, but most of them are not modeled enough to be implemented on humanoid robots. Therefore, first, we conducted gait analysis in this study, and we obtained following two findings: (i) a foot-landing point exists on the line joining the stance leg and the projected point of center of mass on the ground, and (ii) the distance between steps is modified to keep mechanical energy at the landing within a certain value. A walking stabilization control is designed based on the gait analysis. Verification of the proposed control is conducted through experiments with a human-sized humanoid robot WABIAN-2R.